# Auditory Attention Decoding with Task-Related Multi-View Contrastive Learning

arXiv: https://arxiv.org/abs/2308.04244 · HTML: https://mlsanity.com/paper/2308.04244/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2023.

## Built on by (most cited first, 4 of 4)

- [A contrastive-learning approach for auditory attention detection](https://mlsanity.com/paper/2410.18395/) (2024)
- [M^2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation](https://mlsanity.com/paper/2508.00452/) (2025)
- [S2M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection](https://mlsanity.com/paper/2508.05164/) (2025)
- [NEUROTOKEN: Joint Source and Directional AAD with Envelope Decoding via Conditional Flow Matching](https://mlsanity.com/paper/2610.00397/) (2026)

## Abstract (arXiv)

The human brain can easily focus on one speaker and suppress others in scenarios such as a cocktail party. Recently, researchers found that auditory attention can be decoded from the electroencephalogram (EEG) data. However, most existing deep learning methods are difficult to use prior knowledge of different views (that is attended speech and EEG are task-related views) and extract an unsatisfactory representation. Inspired by Broadbent's filter model, we decode auditory attention in a multi-view paradigm and extract the most relevant and important information utilizing the missing view. Specifically, we propose an auditory attention decoding (AAD) method based on multi-view VAE with task-related multi-view contrastive (TMC) learning. Employing TMC learning in multi-view VAE can utilize the missing view to accumulate prior knowledge of different views into the fusion of representation, and extract the approximate task-related representation. We examine our method on two popular AAD datasets, and demonstrate the superiority of our method by comparing it to the state-of-the-art method.